By Stephen Grossberg (auth.), Roberto Pirrone, Filippo Sorbello (eds.)
This e-book constitutes the refereed lawsuits of the twelfth overseas convention of the Italian organization for synthetic Intelligence, AI*IA 2011, held in Palermo, Italy, in September 2011. The 31 revised complete papers awarded including three invited talks and thirteen posters have been rigorously reviewed and chosen from fifty eight submissions. The papers are equipped in topical sections on desktop studying; dispensed AI: robotics and MAS; theoretical concerns: wisdom illustration and reasoning; making plans, cognitive modeling; common language processing; and AI applications.
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Additional info for AI*IA 2011: Artificial Intelligence Around Man and Beyond: XIIth International Conference of the Italian Association for Artificial Intelligence, Palermo, Italy, September 15-17, 2011. Proceedings
K + 1, where the reported MSE is averaged over the k measurements) on training and test data, in function of the number of labeled examples per subject. In the latter, the percentage of points for which f (x) < 0 is also displayed. The two plots in each graph describe the behavior of a classiﬁer in which such constraints were or were not enforced during the training stage. In the group of graphs on top, a radial basis function kernel (RBF) with σ = 10 is used, whereas the group on bottom refers to a polynomial kernel (Poly) of degree 3.
Position top, position middle, . . ). Thus, the original descriptions are to be pre-processed to turn all instances of numeric attributes into the corresponding discretized descriptors. What is a useful number of intervals in which splitting the range of values allowed for a numeric attribute? How to choose the cut points between intervals? 001 for a cut point placed at 5) will be considered as two completely diﬀerent entities. , ), based on statistics on the values occurrence and distribution, although a (partial) manual intervention is often required to provide and/or ﬁx their outcome.
Addis, G. Armano, and E. Vargiu Fig. 1. An example of pipeline (highlighted with bold-dashed lines) PF is a simple categorization technique framed within the local classifier per node approach, which admits only binary decisions, as each classifier is entrusted with deciding whether the input in hand can be forwarded or not to its children. The first proposals in which sequential boolean decisions are applied in combination with local classifiers per node can be found in , , and . In , the idea of mirroring the taxonomy structure through binary classifiers is clearly highlighted; the authors call this technique “binarized structured label learning”.